The short version
AI-300 is Microsoft’s associate-level exam for people who run machine learning and generative AI workloads on Azure. It is less about building a clever model and more about keeping one alive in production: infrastructure as code, deployment, monitoring, evaluation, and tuning. This guide follows Microsoft’s AI-300 study guide on Microsoft Learn, checked on October 18, 2026.
What the exam is
The full name is Operationalizing Machine Learning and Generative AI Solutions. Passing it is the requirement for the Microsoft Certified: Machine Learning Operations Engineer Associate credential, which Microsoft labels intermediate level. Microsoft groups the work under one phrase: AI operations, or AIOps, which combines MLOps (classic machine learning) and GenAIOps (generative AI apps and agents).
In practice the exam splits into two halves. The first half is Azure Machine Learning: workspaces, compute, environments, pipelines, MLflow, endpoints, and monitoring. The second half is Microsoft Foundry: deploying foundation models, managing prompts, evaluating quality and safety, tracing, retrieval-augmented generation (RAG), and fine-tuning. Both halves lean on the same engineering habits, such as Bicep, the Azure CLI, GitHub Actions, managed identities, and private networking.
If you studied for DP-100, treat AI-300 as a different exam. Microsoft’s page for the Azure Data Scientist Associate certification now says that the certification and its renewal assessment are retired. The model lifecycle is still here, but the infrastructure and generative AI material is new.
Who it is for
Microsoft describes a candidate with a data science background, Python experience, and an entry-level understanding of DevOps, including GitHub Actions and command-line tools. You should have worked with Azure Machine Learning to train, deploy, and maintain models, and with Microsoft Foundry to deploy, evaluate, monitor, and optimize generative AI apps and agents. The roles that fit are data scientists moving toward production, DevOps and platform engineers moving into AI, and cloud engineers who support AI teams. Microsoft lists no required prerequisite exam. The prerequisites page goes through what helps.
Format at a glance
| Item | What Microsoft says |
|---|---|
| Exam code | AI-300 |
| Level | Intermediate |
| Time | 120 minutes |
| Passing score | 700 or higher on a scale of 1 to 1,000 |
| Delivery | Proctored, scheduled through Pearson VUE |
| Language | English |
| Number of questions | Not published; Microsoft says most exams have 40 to 60 |
| Interactive parts | The exam page says you may have interactive components |
Details, including scoring and retakes, are on the exam format page.
The five domains and their weightings
| Domain | Weighting |
|---|---|
| Design and implement an MLOps infrastructure | 15-20% |
| Implement machine learning model lifecycle and operations | 25-30% |
| Design and implement a GenAIOps infrastructure | 20-25% |
| Implement generative AI quality assurance and observability | 10-15% |
| Optimize generative AI systems and model performance | 10-15% |
MLOps infrastructure
Everything that has to exist before a model can be trained: Azure Machine Learning workspaces, datastores, compute targets, data assets, environments, components, and registries. It also covers identity and access, network restrictions, and deploying all of this with Bicep, the Azure CLI, and GitHub Actions.
Model lifecycle and operations
The biggest domain. It covers training orchestration (MLflow tracking, automated ML, hyperparameter tuning, distributed training, pipelines), registering and versioning models, deploying real-time and batch endpoints, safe rollout and rollback, data drift detection, and retraining triggers.
GenAIOps infrastructure
The Microsoft Foundry counterpart of the first domain: Foundry resources and projects, managed identities and RBAC, private networking, deploying foundation models, provisioned throughput units, and keeping prompts under version control.
Quality assurance and observability
Evaluation datasets, quality metrics such as groundedness, relevance, coherence, and fluency, safety evaluations, continuous monitoring, latency and token-cost metrics, and tracing and logging for troubleshooting.
Optimizing generative AI systems
Tuning RAG (similarity thresholds, chunk sizes, hybrid search, embedding models) and fine-tuning (methods, synthetic data, monitoring a tuned model through to production).
The three generative AI domains together make up 40 to 55 percent of the exam, so that half is not a side topic.
Official source
For Microsoft’s own description, read the AI-300 study guide. It is the final authority if anything here differs.
More guides for AI-300
- AI-300 Study Plan: 4 and 8 Weeks
- AI-300 Sample Questions With Answers
- Is AI-300 Hard? An Honest Difficulty Check
- AI-300 Renewal and Retakes Explained
- Is AI-300 Worth It? An Honest Look
- AI-300 Cheat Sheet: Key Facts to Review
- AI-200 vs AI-300: Which Exam First?
Next step
Read the domains explained page to see where your time should go. When you want to check yourself, our AI-300 practice tests are five original practice sets, 250 questions in total, spread across these five domains.
Frequently asked questions
Does AI-300 expire?
Microsoft says associate, expert, and specialty certifications expire annually and can be renewed for free with an online assessment on Microsoft Learn. Only fundamentals certifications do not expire.
Is the exam available in other languages?
Microsoft’s exam page lists English only. Check the page again before you book, because that can change.
Is AI-300 the same as DP-100?
No. DP-100 belonged to the Azure Data Scientist Associate certification, which Microsoft now lists as retired. AI-300 covers operations and generative AI in addition to the model lifecycle.